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<span class="breadcrumb-node">Information out: search and analyze</span>
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<span class="prev">
<a href="documents-indices.html">« Data in: documents and indices</a>
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<h2 class="title">
<a id="search-analyze"></a>Information out: search and analyze<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/intro.asciidoc">edit</a>
</h2>
</div></div></div>
<p>While you can use Elasticsearch as a document store and retrieve documents and their
metadata, the real power comes from being able to easily access the full suite
of search capabilities built on the Apache Lucene search engine library.</p>
<p>Elasticsearch provides a simple, coherent REST API for managing your cluster and indexing
and searching your data.  For testing purposes, you can easily submit requests
directly from the command line or through the Developer Console in Kibana. From
your applications, you can use the
<a href="/guide/en/elasticsearch/client/index.html" class="ulink" target="_top">Elasticsearch client</a>
for your language of choice: Java, JavaScript, Go, .NET, PHP, Perl, Python
or Ruby.</p>
<h4>
<a id="search-data"></a>Searching your data<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/intro.asciidoc">edit</a>
</h4>
<p>The Elasticsearch REST APIs support structured queries, full text queries, and complex
queries that combine the two. Structured queries are
similar to the types of queries you can construct in SQL. For example, you
could search the <code class="literal">gender</code> and <code class="literal">age</code> fields in your <code class="literal">employee</code> index and sort the
matches by the <code class="literal">hire_date</code> field. Full-text queries find all documents that
match the query string and return them sorted by <em>relevance</em>—how good a
match they are for your search terms.</p>
<p>In addition to searching for individual terms, you can perform phrase searches,
similarity searches, and prefix searches, and get autocomplete suggestions.</p>
<p>Have geospatial or other numerical data that you want to search? Elasticsearch indexes
non-textual data in optimized data structures that support
high-performance geo and numerical queries.</p>
<p>You can access all of these search capabilities using Elasticsearch’s
comprehensive JSON-style query language (<a class="xref" href="query-dsl.html" title="Query DSL">Query DSL</a>). You can also
construct <a class="xref" href="sql-overview.html" title="Overview">SQL-style queries</a> to search and aggregate data
natively inside Elasticsearch, and JDBC and ODBC drivers enable a broad range of
third-party applications to interact with Elasticsearch via SQL.</p>
<h4>
<a id="analyze-data"></a>Analyzing your data<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/intro.asciidoc">edit</a>
</h4>
<p>Elasticsearch aggregations enable you to build complex summaries of your data and gain
insight into key metrics, patterns, and trends. Instead of just finding the
proverbial “needle in a haystack”, aggregations enable you to answer questions
like:</p>
<div class="ulist itemizedlist">
<ul class="itemizedlist">
<li class="listitem">
How many needles are in the haystack?
</li>
<li class="listitem">
What is the average length of the needles?
</li>
<li class="listitem">
What is the median length of the needles, broken down by manufacturer?
</li>
<li class="listitem">
How many needles were added to the haystack in each of the last six months?
</li>
</ul>
</div>
<p>You can also use aggregations to answer more subtle questions, such as:</p>
<div class="ulist itemizedlist">
<ul class="itemizedlist">
<li class="listitem">
What are your most popular needle manufacturers?
</li>
<li class="listitem">
Are there any unusual or anomalous clumps of needles?
</li>
</ul>
</div>
<p>Because aggregations leverage the same data-structures used for search, they are
also very fast. This enables you to analyze and visualize your data in real time.
Your reports and dashboards update as your data changes so you can take action
based on the latest information.</p>
<p>What’s more, aggregations operate alongside search requests. You can search
documents, filter results, and perform analytics at the same time, on the same
data, in a single request. And because aggregations are calculated in the
context of a particular search, you’re not just displaying a count of all
size 70 needles, you’re displaying a count of the size 70 needles
that match your users' search criteria—​for example, all size 70 <em>non-stick
embroidery</em> needles.</p>
<h5>
<a id="more-features"></a>But wait, there’s more<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/intro.asciidoc">edit</a>
</h5>
<p>Want to automate the analysis of your time-series data? You can use
<a href="/guide/en/machine-learning/7.7/ml-overview.html" class="ulink" target="_top">machine learning</a> features to create accurate
baselines of normal behavior in your data and identify anomalous patterns. With
machine learning, you can detect:</p>
<div class="ulist itemizedlist">
<ul class="itemizedlist">
<li class="listitem">
Anomalies related to temporal deviations in values, counts, or frequencies
</li>
<li class="listitem">
Statistical rarity
</li>
<li class="listitem">
Unusual behaviors for a member of a population
</li>
</ul>
</div>
<p>And the best part? You can do this without having to specify algorithms, models,
or other data science-related configurations.</p>
</div>
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